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    Y Cli

    A Tiny Terminal Chat App for AI Models with MCP Client Support Python-based implementation.

    189 stars
    Python
    Updated Oct 19, 2025

    Table of Contents

    • Demo
    • Context
    • Always-on
    • Orchestration
    • Docs
    • Blog Post

    Table of Contents

    • Demo
    • Context
    • Always-on
    • Orchestration
    • Docs
    • Blog Post

    Documentation

    y-agent

    A personal AI agent system built on top of coding agents.

    Renamed from y-cli. y-cli wrapped model APIs; y-agent wraps coding agents.

    Demo

    y-agent TraceView

    A real trace: https://yovy.app/t/6fc5c4

    Web chat renders inline artifacts from assistant messages: Mermaid diagrams, Vega-Lite charts, and sanitized artifact-svg blocks.

    ---

    Coding agents like Claude Code / Codex are great for code, but code is only part of my daily life. I also have ledgers, calendars, todos, notes, emails. I want the agent to handle those too.

    Three things came up while extending a coding agent into a personal agent system:

    1. How to give the agent context

    2. How to keep the agent always-on

    3. How to orchestrate multiple agents

    Context

    Same data for me and for the agent. Files go through read / write / edit. Anything I'd reach for a GUI to do, the agent reaches for a CLI — it's already happy in Bash. Rule: whatever I can do in the GUI, the agent can do via CLI. The underlying file or DB row is the same.

    Always-on

    I don't want to carry a laptop or open a terminal to use it. Coding agents run on a remote VM (EC2) inside tmux; a tail process parses their output into the database, so the web UI can chat with them directly. A Telegram bot covers mobile input. EC2 auto-hibernates when idle, so cost is near zero when nothing is running.

    Orchestration

    One session usually can't handle the whole thing — requests have to be routed to the right session. Claude Code ships sub-agents, but I wanted that layer outside, so sub-agent chats stay in my own DB and I can steer them mid-run.

    code
    user        ┌──────────────────┐
       input ────► │  skill: manager  │   dispatch only,
            │     └────────┬──────────┘   no execution
            │              │   y chat --skill dev -m "..."
            │              ▼
            │     ┌──────────────────┐
            ├───► │  skill: dev      │   coordinator,
            │     │                  │   runs lower-level skill sessions
            │     └──┬──────┬──────┬─┘
            │        │      │      │   y chat --skill {plan,impl,review}
            │        ▼      ▼      ▼
            │     ┌──────┐ ┌──────┐ ┌────────┐
            └───► │ plan │ │ impl │ │ review │   anonymous, ephemeral;
                  └──────┘ └──────┘ └────────┘   skill loaded per dispatch

    A trace_id (= todo_id when the task is tracked) threads the whole tree, so TraceView renders the chain as a waterfall.

    Docs

    Two paths, depending on whether you want to run your own instance:

    Use the hosted instance — y login against yovy.app and go.

    • docs/cli.md — install the CLI, sign in, common commands.
    • docs/getting-started.md — what the web UI looks like after sign-in.
    • docs/capabilities.md — what subsystems ship in a running deployment.
    • y cookies sync uploads local browser cookies for remote link fetchers such as YouTube.

    Self-host — run the API + worker yourself.

    • docs/self-host.md — prerequisites, install, run, deploy, config keys.

    Blog Post

    Longer write-up, design rationale, and comparisons: full blog post.

    CHANGELOG tracks weekly updates.

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